Learning and retaining visuomotor adaptation across time
Bibliographic record
Abstract
Many studies have shown that the brain can learn to adjust movements to correct for altered visual feedback of the hand when reaching to targets. It is unclear how well brain can learn these adjustments when there are significant gaps in time between brief sessions compared to a single session and how long visuomotor learning is retained. How do delays in time affect learning and later retrieving a novel visuomotor mapping? In Study 1, participants adapted to altered visual feedback of the hand when reaching to visual targets in a single session of 100 trials and across five shorter weekly sessions of 20 trials each. In Study 2, another group of participants learned a similar visuomotor mapping across 200 trials and were retested on the same task 6–8 weeks, 2–3 or 5–6 months later. Results for Study 1 showed that participants had similar learning patterns for a single session compared to weekly sessions. We saw no significant difference in the overall learning rate, and the gaps between sessions did not lead to any loss of previous learning. This indicates that the brain does not need continuous reaching practice but rather it can adapt to this new visuomotor mapping with 7-day gaps between shorter reaching sessions. Preliminary results for Study 2 show that participants retained a substantial amount of visuomotor adaptation over time. We found that deviations in reaching were smaller when participants performed the same task 6–8 weeks after the first visuomotor adaptation session. Our results suggest that the learned visuomotor mapping may last over longer time frames of 2–3, and 5–6 months. We are currently collecting these data. Our results from Study 1 and 2 suggest that the brain is able to learn and retain visuomotor adaptations over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".